Containerized microservices have been the gold standard for cloud computing since they replaced the monolith architecture over a decade ago. The flexibility, scalability, and velocity they enable for teams make them an obvious choice. Yet, a strict interpretation of one service for one function doesn’t quite serve everyone, especially when architectures get large. We’ll discuss how flexibility in service architecture might be the way to go.

Software complexity makes it harder for teams to rapidly identify and resolve issues. IT service management has evolved from an afterthought to a central part of DevOps. Microservices architectures are prone to delay or missed identification of such concerns. Monitoring mechanisms need to keep up with these complex infrastructures. Maintaining reliability and performance while harnessing this complexity requires a considered, data-driven approach.

From its initial appearance in the dev-tools space, GenAI has had an outsized impact on how developers approach day-to-day tasks (just ask any developer about when they first started using GitHub’s copilot). While any risks are still being evaluated—like potential for introducing anti-patterns or inadvertently running afoul of compliance requirements, many engineering teams have successfully implemented GenAI with measurable gains in collaboration and productivity.

Companies obsess over end user experience, whether it is Amazon’s customer-centric innovation or Steve Jobs suggesting starting with the customer experience and working backwards to technology. But as our world becomes more knowledge-based and digital, we also need to consider the most important stakeholder on the payroll - software engineers.

Observability tools help engineering teams understand the health and behavior of software. But the term “health” in the context of this type of tooling is fairly narrow in scope—pertaining to real-time performance, reliability, and availability. While these are three important metrics to monitor, they’re lagging indicators of bigger issues happening upstream.

You might have heard of Spotify Backstage (backstage.io), an open-source option for building internal developer portals. But what is Spotify Backstage? This article will help answer your questions about Backstage and explain important considerations to think about if you’re deciding whether to adopt in your team.

Quality engineering (QE), or software quality engineering (SQE), is a discipline within software development focused on ensuring the quality, reliability, and performance of software products. With an increase in development environment complexity in recent years, the focus has shifted back from detecting defects in later stages, as QA has typically done, to proactively ensuring quality throughout the entire development lifecycle.

Reliability metrics in software development are metrics that help teams quantify how dependable and consistent their software systems are over time. By converting a wide range of technical properties into hard data, these provide quantifiable information to understand the probability of software running failure-free in a given environment over time. These metrics are a subset of developer-focused key performance indicators (KPIs), data that is gathered to emphasize developers' output.

Software development is a sophisticated process that comes with complexity built in. Enter DevOps (many years ago, of course) to foster an environment where developers can build complex applications while minimizing backend overhead, ensuring that processes are replicable and software design is standardized. One of the first examples of DevOps in action was the surge in popularity for service-oriented architecture.

Software engineering key performance indicators (KPIs) help engineer leadings keep teams accountable while ensuring focus on highest leverage activities. They are essential for driving process improvement, managing risks, supporting data-driven decision making, and ensuring customer satisfaction. Without KPIs, teams may encounter challenges related to visibility, efficiency, decision-making, quality, and customer satisfaction, which can ultimately impact project success and organizational performance.